Claude + SimilarWeb MCP

Apply when: sizing a category before committing budget, preparing a competitive review for a client or board, deciding which channel a competitor actually grows on, or working out whether a market is worth entering at all.

Most of a market research week is not analysis — it is fetching, pasting and reformatting numbers. Give the model direct access to the data source and that half of the week collapses, leaving the half that was always the point.

MCP — the Model Context Protocol — is a standard way to hand a model a live tool instead of a paragraph of pasted text. With a SimilarWeb MCP server connected, "how much traffic does this competitor get, and where from" stops being a task a person does between thoughts and becomes a call the model makes mid-investigation.

The difference is not that the model is smarter. It is that the model can now follow a question. When a channel split looks strange, it can pull the next domain immediately rather than returning a list of things for you to go and look up.

The input is deliberately small: a handful of competitor URLs and one sentence saying what decision the report is meant to support.

1 · Frame the decision. Not "research this market" but "we are deciding whether paid search is defensible here." An unframed brief produces a report that is broad and useless.

2 · Seed the set. Three to six known competitors. The model expands the set itself from referral and similar-site data.

3 · Pull and compare. Volume, trend, channel mix, geography, top pages. The comparison across the set matters far more than any single figure.

4 · Chase the anomaly. The useful finding is almost always a mismatch — one competitor whose channel mix does not look like anyone else's. This is the step that a manual process rarely reaches, because by then the week is gone.

5 · Write it up. One HTML file: the decision it supports, what the data shows, what it does not show, and the recommendation.

A useful version of this report is short and opinionated: the category shape, the traffic ranking with the trend direction beside it, the channel mix per competitor, where each one is strongest geographically, and the two or three anomalies worth acting on.

What it should not contain is every chart the tool can produce. The failure mode of an automated research workflow is volume — a fifty-page document nobody reads, produced in an afternoon instead of a week, which is not an improvement.

The numbers are estimates. Panel-based traffic data is modelled, not measured. It is reliable for relative comparison between sites of similar size and unreliable as an absolute figure. A report that quotes a competitor's monthly visits to three significant figures is overstating what the data can carry.

Small sites are noise. Below a certain traffic threshold the estimates swing hard between months. Early-stage competitors are exactly the ones you most want to measure and exactly the ones the data handles worst.

The model will not doubt a number it cannot check. Given a figure by a tool, it treats it as fact. Every claim that will end up in front of a client needs a human to ask whether the underlying estimate is plausible.

Speed is not the deliverable. Producing the report in hours only helps if someone still spends real time deciding what it means. The hours saved belong to the interpretation, not to the calendar.

This is the research half of AI-native consultancy — category intelligence produced fast enough to be run before a decision rather than after it. The same loop, pointed at creative rather than traffic, is what sits behind our market playbooks.